ServicesCloud & AI4–12 weeks

AI features that hold up outside the demo.

An LLM prototype takes an afternoon and looks finished. What it hides is everything that decides whether it survives contact with users: what happens on the query nobody anticipated, how you know quality dropped after a model update, what a hostile input can talk it into, and what it costs at a thousand times the traffic.

We build the unglamorous half. Evals that run like tests so a prompt change has a measurable effect, retrieval tuned against your own documents rather than a generic benchmark, guardrails against prompt injection and PII leakage, and per-endpoint cost and latency limits. The feature ships when it can be measured, not when it demos well.

  • LLM integration
  • Vector search
  • Agent workflows
  • Evals & guardrails

How we work

01

Write the eval set first

A hundred real queries with known-good answers, before any prompt engineering. Without it, every change is judged on vibes and nobody can tell improvement from regression.

02

Retrieval is the product

Most bad LLM answers are bad retrieval wearing a confident voice. Chunking, embeddings, and reranking get tuned against your corpus — the model choice matters less than people expect.

03

Treat inputs as hostile

Prompt injection is the SQL injection of this decade. Untrusted content never carries authority, tool calls are scoped, and PII is redacted before it reaches a third-party endpoint.

04

Watch drift in production

Quality scores, latency, and spend on a live dashboard. Providers change models underneath you; you want to find that out from a chart, not from a customer.

What you get

  • RAG pipeline tuned to your domain with quality evals
  • Agent workflows with tool use and guardrails
  • Prompt-injection defense and PII redaction
  • Live quality dashboard so you catch drift early
  • Cost and latency guardrails per endpoint

Ideal for

Product teams who want AI features that are actually reliable — not demoware.

When it’s not us

Not a fit if the goal is to have shipped something with AI in it. Without a task worth measuring, this becomes an expensive way to add a chat box.

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